Exploding the myths: An introduction to artificial neural networks for prediction and forecasting

Holger R. Maier, Stefano Galelli, Saman Razavi, Andrea Castelletti, Andrea Emilio Rizzoli, Ioannis N. Athanasiadis, Miquel Sànchez-Marrè, Marco Acutis, Wenyan Wu, Greer B. Humphrey

Abstract: Highlights•Commonly held myths about artificial neural network (ANN) modelling are articulated and busted.•Good modelling practices applied to other types of predictive models are also applicable to ANNs.•State-of-the-art approaches to developing ANN models for prediction and forecasting are presented.•Size matters – ANNs with more inputs and hidden nodes are more likely to give non-sensical results.•Using an independent validation set is insufficient to ensure ANNs can be used with confidence in practice.